Spam Email Classification Using NLP

Overview

This project focuses on classifying emails as spam or not spam using Natural Language Processing (NLP) techniques. The repository includes code for training models, evaluating their performance, and a dataset for experimentation.

Contents

• models/: Directory containing trained models.
• src/ : Directory containing source files
  • FFNN.py: Script for training a Feedforward Neural Network.
  • main.py: Main script for running the classification.
  • utils.py: Utility functions for data processing and model evaluation.
• data/: Directory containing the data set
  • Oppositional_thinking_analysis_dataset.json: Dataset used for training and evaluation.
• desc/: Directory containing the description of the project
  • NLP - Project 1_4.pdf: Project documentation and analysis.

Requirements

• Python 3.x
• Necessary libraries listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/damlakayikci/Spam-Email-Classification-NLP.git
   cd src
   

Usage

Use one of the following scripts to run the code

1. To train and run the Naive Bayes model:

   python main.py nb

2. To train and run the Feedforward Neural Network (FFNN):

   python main.py ffnn

3. To print statistics and plot graphs:

   python main.py stats

4. To find the Pointwise Mutual Information (PMI) of 10 random words and print the most similar words:

   python main.py pmi